Maximizing Roster Efficiency in the MLS
Introduction
Data
| The Top 5 MLS Players 2021–2024 Based on G+ Per 90 min. | |||||
|---|---|---|---|---|---|
| Player, Year | Club | Position | Age | Nationality | G+ Per 90 min. |
| Lionel Messi, 2024 | Inter Miami | W | 37 | Argentina | 0.572 |
| Cucho Hernandez, 2024 | Columbus Crew | ST | 25 | Colombia | 0.471 |
| Adam Buksa, 2021 | New England Revolution | ST | 25 | Poland | 0.455 |
| Riqui Puig, 2024 | LA Galaxy | CM | 25 | Spain | 0.455 |
| Cucho Hernandez, 2023 | Columbus Crew | ST | 24 | Colombia | 0.451 |
Table 1. The best MLS players from 2021-2024 based on their goals added per 90 minutes played. This top 5 proves that goals added is a valuable metric.
| The Top 5 Most Efficient Player Seasons in the MLS in the last 4 Years | |||||
|---|---|---|---|---|---|
| Player, Year | Club | Position | Age | Nationality | GA_Per_90_Per_10k |
| Patrick Agyemang, 2024 | Charlotte FC | ST | 23 | USA | 0.0475 |
| Célio Pompeu, 2023 | St. Louis City | W | 23 | Brazil | 0.0462 |
| Tani Oluwaseyi, 2024 | Minnesota United | ST | 24 | Canada | 0.0411 |
| Jacob Murrell, 2024 | D.C. United | ST | 20 | USA | 0.0369 |
| Fredy Montero, 2021 | Seattle Sounders | ST | 34 | Colombia | 0.0350 |
Table 2. The most efficient MLS players from 2021-2024 based on their goals added per 90 minutes played per $10k they are paid.
Methods
Results
Discussion
Appendix
| Term | Estimate | SE | t | p |
|---|---|---|---|---|
| (Intercept) | 0.90351 | 7.65199 | 0.11808 | 0.90688 |
| avg_guaranteed_compensation | 0.00000 | 0.00001 | -0.12225 | 0.90361 |
| Term | Estimate | SE | t | p |
|---|---|---|---|---|
| (Intercept) | -24.48032 | 6.56203 | -3.73060 | 3e-04 |
| total_goals_added_for | 1.12311 | 0.10015 | 11.21482 | 0e+00 |
| Variable 1 | Variable 2 | Correlation |
|---|---|---|
| total_goals_added_for | points | 0.73 |
| Term | Estimate | SE | t | p |
|---|---|---|---|---|
| (Intercept) | 0.04572 | 0.00239 | 19.13799 | 0.00000 |
| fwd_def_spend_ratio | -0.00253 | 0.00095 | -2.65173 | 0.00918 |
| Model | Mean RMSE | SD |
|---|---|---|
| enet | 9.94 | 1.26 |
| lasso | 9.89 | 1.24 |
| lm | 9.89 | 1.24 |
| ridge | 10.12 | 1.25 |
| xgb | 12.89 | 1.60 |
| Term | Estimate | SE | t | p |
|---|---|---|---|---|
| (Intercept) | -17.18 | 6.59 | -2.61 | 0.01 |
4-6 |
0.32 | 0.39 | 0.83 | 0.41 |
7-9 |
-0.08 | 0.87 | -0.10 | 0.92 |
10-12 |
2.40 | 1.32 | 1.81 | 0.07 |
13-15 |
-0.34 | 1.60 | -0.21 | 0.83 |
16-18 |
-1.78 | 1.25 | -1.42 | 0.16 |
Adjusted R-squared: 0.131
Model p-value: 0.006
| Position | Term | Estimate | SE | t | p |
|---|---|---|---|---|---|
| AM | (Intercept) | 0.19307 | 0.01371 | 14.08591 | 0.00000 |
| AM | guaranteed_compensation | 0.00000 | 0.00000 | 2.47279 | 0.01621 |
| CB | (Intercept) | 0.13618 | 0.00393 | 34.68045 | 0.00000 |
| CB | guaranteed_compensation | 0.00000 | 0.00000 | 2.45097 | 0.01472 |
| CM | (Intercept) | 0.13525 | 0.00665 | 20.33777 | 0.00000 |
| CM | guaranteed_compensation | 0.00000 | 0.00000 | 3.23632 | 0.00143 |
| DM | (Intercept) | 0.12874 | 0.00578 | 22.26634 | 0.00000 |
| DM | guaranteed_compensation | 0.00000 | 0.00000 | 4.19331 | 0.00004 |
| FB | (Intercept) | 0.11539 | 0.00402 | 28.73566 | 0.00000 |
| FB | guaranteed_compensation | 0.00000 | 0.00000 | 4.66337 | 0.00000 |
| ST | (Intercept) | 0.22152 | 0.00944 | 23.46965 | 0.00000 |
| ST | guaranteed_compensation | 0.00000 | 0.00000 | 1.61896 | 0.10728 |
| W | (Intercept) | 0.18454 | 0.00597 | 30.90898 | 0.00000 |
| W | guaranteed_compensation | 0.00000 | 0.00000 | 2.77011 | 0.00608 |
| Term | Estimate | SE | t | p | 95% CI Low | 95% CI High |
|---|---|---|---|---|---|---|
| (Intercept) | -556924.45 | 63942.642 | -8.70975 | 0.00000 | -682350.462 | -431498.44 |
| age | 32072.86 | 2326.887 | 13.78359 | 0.00000 | 27508.579 | 36637.14 |
| general_positionCB | 90841.52 | 27886.941 | 3.25749 | 0.00115 | 36140.187 | 145542.86 |
| general_positionCM | 204584.75 | 33064.797 | 6.18739 | 0.00000 | 139726.844 | 269442.65 |
| general_positionDM | 216203.72 | 34260.971 | 6.31050 | 0.00000 | 148999.476 | 283407.97 |
| general_positionST | 323932.68 | 34220.838 | 9.46595 | 0.00000 | 256807.153 | 391058.20 |
| general_positionW | 232919.43 | 32080.337 | 7.26050 | 0.00000 | 169992.587 | 295846.28 |
| general_positionAM | 354967.26 | 50307.313 | 7.05598 | 0.00000 | 256287.483 | 453647.04 |
| region_groupSouth America | 179660.95 | 24445.009 | 7.34960 | 0.00000 | 131711.101 | 227610.80 |
| region_groupCentral America/Caribbean | -36228.70 | 41423.290 | -0.87460 | 0.38193 | -117482.112 | 45024.72 |
| region_groupEurope | 248329.42 | 25515.469 | 9.73250 | 0.00000 | 198279.820 | 298379.02 |
| region_groupAfrica | 92337.88 | 39352.968 | 2.34640 | 0.01908 | 15145.482 | 169530.28 |
| region_groupAsia/Oceania | 138940.39 | 72030.606 | 1.92891 | 0.05393 | -2350.481 | 280231.26 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.25863 | 0.25272 | 363048.9 | 43.80985 | 0 | 12 | -21609.74 | 43247.49 | 43322.06 | 1.986294e+14 | 1507 | 1520 |
| Term | Estimate | SE | t | p | 95% CI Low | 95% CI High |
|---|---|---|---|---|---|---|
| (Intercept) | 0.11462 | 0.00896 | 12.78784 | 0.00000 | 0.09704 | 0.13221 |
| age | 0.00036 | 0.00033 | 1.10531 | 0.26920 | -0.00028 | 0.00100 |
| general_positionCB | 0.01104 | 0.00391 | 2.82340 | 0.00481 | 0.00337 | 0.01871 |
| general_positionCM | 0.02092 | 0.00464 | 4.51241 | 0.00001 | 0.01182 | 0.03001 |
| general_positionDM | 0.01349 | 0.00480 | 2.80803 | 0.00505 | 0.00407 | 0.02291 |
| general_positionST | 0.10028 | 0.00480 | 20.90496 | 0.00000 | 0.09087 | 0.10969 |
| general_positionW | 0.06296 | 0.00450 | 14.00058 | 0.00000 | 0.05414 | 0.07178 |
| general_positionAM | 0.08546 | 0.00705 | 12.11784 | 0.00000 | 0.07162 | 0.09929 |
| region_groupSouth America | 0.02050 | 0.00343 | 5.98336 | 0.00000 | 0.01378 | 0.02723 |
| region_groupCentral America/Caribbean | 0.01178 | 0.00581 | 2.02833 | 0.04270 | 0.00039 | 0.02317 |
| region_groupEurope | 0.01319 | 0.00358 | 3.68892 | 0.00023 | 0.00618 | 0.02021 |
| region_groupAfrica | 0.00045 | 0.00552 | 0.08154 | 0.93502 | -0.01037 | 0.01127 |
| region_groupAsia/Oceania | 0.02227 | 0.01010 | 2.20572 | 0.02755 | 0.00247 | 0.04208 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3408 | 0.33555 | 0.05089 | 64.92593 | 0 | 12 | 2376.352 | -4724.704 | -4650.134 | 3.90325 | 1507 | 1520 |
| Term | Estimate | SE | t | p | 95% CI Low | 95% CI High |
|---|---|---|---|---|---|---|
| (Intercept) | 0.01859 | 0.00092 | 20.15950 | 0.00000 | 0.01678 | 0.02040 |
| age | -0.00045 | 0.00003 | -13.46425 | 0.00000 | -0.00052 | -0.00039 |
| general_positionCB | 0.00045 | 0.00040 | 1.11769 | 0.26388 | -0.00034 | 0.00124 |
| general_positionCM | -0.00063 | 0.00048 | -1.31558 | 0.18851 | -0.00156 | 0.00031 |
| general_positionDM | -0.00047 | 0.00049 | -0.95713 | 0.33866 | -0.00144 | 0.00050 |
| general_positionST | 0.00129 | 0.00049 | 2.60438 | 0.00929 | 0.00032 | 0.00225 |
| general_positionW | 0.00093 | 0.00046 | 2.01465 | 0.04412 | 0.00002 | 0.00184 |
| general_positionAM | 0.00029 | 0.00073 | 0.40469 | 0.68577 | -0.00113 | 0.00172 |
| region_groupSouth America | -0.00276 | 0.00035 | -7.84113 | 0.00000 | -0.00346 | -0.00207 |
| region_groupCentral America/Caribbean | 0.00033 | 0.00060 | 0.54555 | 0.58546 | -0.00085 | 0.00150 |
| region_groupEurope | -0.00276 | 0.00037 | -7.50566 | 0.00000 | -0.00348 | -0.00204 |
| region_groupAfrica | -0.00093 | 0.00057 | -1.63059 | 0.10319 | -0.00204 | 0.00019 |
| region_groupAsia/Oceania | -0.00242 | 0.00104 | -2.33306 | 0.01978 | -0.00446 | -0.00039 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.18316 | 0.17665 | 0.00524 | 28.15897 | 0 | 12 | 5832.871 | -11637.74 | -11563.17 | 0.04133 | 1507 | 1520 |